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A critical component of sophisticated image processing is the use of deep learning algorithms for the diagnosis of glaucoma. The input photos are standardized to 224 × 224 pixels during the pre-processing stage, and data augmentation techniques are used to increase the resilience of the model. Specifically trained to extract features from fundus images, the VGG19 model, Inception Net, and Resnet50 are integrated with Convolutional Neural Network (CNN) features in the suggested approach. This approach’s main goal is to improve the precision and efficacy of glaucoma identification in medical pictures, enabling early glaucoma detection and diagnosis, supporting clinic decision-making, and enhancing patient health.

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Glaucoma Diagnosis: Harnessing VGG19 in Convolutional Neural Networks for Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Vidyullatha Sukhavasi,
  • M. Shanmuga Sundari,
  • Malaya Nayak,
  • T. Chandana,
  • I. Akshitha,
  • D. Sharwani

摘要

A critical component of sophisticated image processing is the use of deep learning algorithms for the diagnosis of glaucoma. The input photos are standardized to 224 × 224 pixels during the pre-processing stage, and data augmentation techniques are used to increase the resilience of the model. Specifically trained to extract features from fundus images, the VGG19 model, Inception Net, and Resnet50 are integrated with Convolutional Neural Network (CNN) features in the suggested approach. This approach’s main goal is to improve the precision and efficacy of glaucoma identification in medical pictures, enabling early glaucoma detection and diagnosis, supporting clinic decision-making, and enhancing patient health.